Intelligent dishwashing systems and methods

ABSTRACT

Example intelligent dishwashing systems and methods are described. In one implementation, a system includes an imaging system configured to capture at least one image of an article of dishware. A processing system analyzes the at least one image to determine a presence and a location of a stain on the article of dishware. Responsive to determining the presence of a stain, a cleaning system is configured to clean the location of the stain on the article of dishware.

TECHNICAL FIELD

The present disclosure relates to dishwashing systems and methods thatuse imaging systems and associated image analysis methods to determine apresence of a stain or a defect on an article of dishware.

BACKGROUND

A process of automated dishwashing entails an article of dishware beingplaced into a dishwashing system, where the article of dishware istypically washed and rinsed with a combination of water and othercleaning agents. In some cases, a single wash cycle may be insufficientto remove all existing soils or stains on the article of dishware.Furthermore, the dishwashing system may not be configured to detectwhether the article of dishware is stained or soiled (either pre-wash orpost-wash), or whether the article of dishware is damaged or defective.There exists a need, therefore, for an intelligent dishwashing systemthat is enabled to determine a presence of a stain or soiling on anarticle of dishware during a pre-wash or post-wash phase, or to detectwhether an article of dishware is defective or damaged.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present disclosureare described with reference to the following figures, wherein likereference numerals refer to like parts throughout the various figuresunless otherwise specified.

FIG. 1A is a schematic diagram depicting an embodiment of a staindetection system.

FIG. 1B is a schematic diagram depicting another embodiment of a staindetection system.

FIG. 2 is a block diagram depicting an embodiment of a processing systemcapable of operating a stain detection system configured to detect astain on an article of dishware.

FIG. 3 is a block diagram depicting an embodiment of an image analysissystem.

FIG. 4 is a schematic diagram depicting an embodiment of a targetedcleaning system.

FIGS. 5A-5E are schematic diagrams depicting examples of data labelingand annotation associated with operation of an embodiment of a staindetection system.

FIG. 6 is a flow diagram depicting an embodiment of a method todetermine whether an article of dishware is dirty.

FIG. 7 is a flow diagram depicting an embodiment of a method todetermine whether an article of dishware is defective.

FIG. 8 is a flow diagram depicting an embodiment of a method to train aneural network to perform a function of stain detection.

FIG. 9 is a flow diagram depicting an embodiment of a method that uses atrained neural network to perform a function of stain detection andpresent a level of dirtiness as an output classification.

FIG. 10 is a flow diagram depicting an embodiment of a method that usesa trained neural network to perform a function of stain detection andgenerate a dirt segmentation output.

FIG. 11 is a schematic diagram depicting an embodiment of arecirculation system associated with articles of dishware.

FIG. 12 is a block diagram depicting an embodiment of a processingsystem capable of operating a recirculation system that is configured todetermine whether an article of dishware that has been washed is stilldirty, and send the article of dishware back for additional cleaning ifthe article of dishware is determined to be dirty.

FIG. 13 is a block diagram depicting an embodiment of a lightingcontroller.

FIG. 14 is a block diagram depicting an embodiment of a spray nozzlecontroller.

FIGS. 15A and 15B are flow diagrams depicting an embodiment of a methodof recirculating articles of dishware.

FIG. 16 is a flow diagram depicting an embodiment of a method to performtargeted cleaning on an article of dishware.

DETAILED DESCRIPTION

In the following disclosure, reference is made to the accompanyingdrawings, which form a part hereof, and in which is shown by way ofillustration specific implementations in which the disclosure may bepracticed. It is understood that other implementations may be utilizedand structural changes may be made without departing from the scope ofthe present disclosure. References in the specification to “oneembodiment,” “an embodiment,” “an example embodiment,” etc., indicatethat the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to affect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

Implementations of the systems, devices, and methods disclosed hereinmay comprise or utilize a special purpose or general-purpose computerincluding computer hardware, such as, for example, one or moreprocessors and system memory, as discussed herein. Implementationswithin the scope of the present disclosure may also include physical andother computer-readable media for carrying or storingcomputer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arecomputer storage media (devices). Computer-readable media that carrycomputer-executable instructions are transmission media. Thus, by way ofexample, and not limitation, implementations of the disclosure cancomprise at least two distinctly different kinds of computer-readablemedia: computer storage media (devices) and transmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,solid state drives (“SSDs”) (e.g., based on RAM), Flash memory,phase-change memory (“PCM”), other types of memory, other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store desired program code means inthe form of computer-executable instructions or data structures andwhich can be accessed by a general purpose or special purpose computer.

An implementation of the devices, systems, and methods disclosed hereinmay communicate over a computer network. A “network” is defined as oneor more data links that enable the transport of electronic data betweencomputer systems and/or modules and/or other electronic devices. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a transmission medium. Transmissions media can include anetwork and/or data links, which can be used to carry desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer. Combinations of the above should also be includedwithin the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. The computerexecutable instructions may be, for example, binaries, intermediateformat instructions such as assembly language, or even source code.Although the subject matter is described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described herein.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, an in-dash vehicle computer, personalcomputers, desktop computers, laptop computers, message processors,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, mobile telephones, PDAs, tablets, pagers, routers, switches,various storage devices, and the like. The disclosure may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Further, where appropriate, functions described herein can be performedin one or more of: hardware, software, firmware, digital components, oranalog components. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. Certain terms are usedthroughout the description and claims to refer to particular systemcomponents. As one skilled in the art will appreciate, components may bereferred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

It should be noted that the sensor embodiments discussed herein maycomprise computer hardware, software, firmware, or any combinationthereof to perform at least a portion of their functions. For example, asensor may include computer code configured to be executed in one ormore processors, and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein purposes of illustration, and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices, as would be known to persons skilled in the relevantart(s).

At least some embodiments of the disclosure are directed to computerprogram products comprising such logic (e.g., in the form of software)stored on any computer useable medium. Such software, when executed inone or more data processing devices, causes a device to operate asdescribed herein.

The systems and methods described herein use one or more imaging systemsand associated image analysis techniques to determine a presence of astain or a defect on an article of dishware. In some embodiments, imageanalysis is performed by convolutional neural networks (CNNs).Particular embodiments include different light sources configured toilluminate an article of dishware to be imaged, or a plurality ofoptical filters configured to modify light illuminating the article ofdishware or light reflected by the article of dishware. Some embodimentsmay include one or more robotic actuators configured to manipulate anarticle of dishware to be imaged, as described herein.

FIG. 1A is a schematic diagram depicting an embodiment of a staindetection system 100. In some embodiments, stain detection system 100includes a light source 102 that is configured to illuminate an articleof dishware 110. Article of dishware 110 is illuminated via an incidentlight path 112 corresponding to light emitted by light source 102. Anoptical filter 108 that is configured to modify characteristics of lightemitted by light source 102 (such as light color, transmittedwavelength(s), or light intensity) may be placed in incident light path112. (In other words, optical filter 108 is optically coupled to lightsource 102.) Light emitted by light source 102 incident on article ofdishware 110 after passing through optical filter 108 is reflected in aview of an imaging system 104 via a reflected light path 114. An opticalfilter 115 may be introduced into reflected light path 114 to modifycharacteristics (such as light color, transmitted wavelength(s), orlight intensity) of light traveling along reflected light path 114. (Inother words, optical filter 115 is optically coupled to imaging system104.) Examples of light sources such as light source 102, opticalfilters such as optical filter 108 and optical filter 115, and imagingsystems such as imaging system 104 are provided herein.

In some embodiments, light traveling along reflected light path 114 isused by imaging system 104 after passing through optical filter 115, tocapture one or more images of article of dishware 110. A processingsystem 106 coupled to imaging system 104 receives the one or moreimages, and processes the one or more images to determine (or detect) apresence of a stain or a defect on article of dishware 110. The term“stain” may be used interchangeably herein with terms such as “dirt”,“spot”, “soils”, or other kinds of similar descriptors to designatesoiling or stains on an article of dishware. Methods used by processingsystem 106 to accomplish stain detection or defect detection on articleof dishware 110 are described herein. Stain detection system 100 may beused to assist an automated dishwashing machine in characterizing one ormore stains on an article of dishware, thereby adding intelligence toassociated dishwashing operations.

In some embodiments, processing system 106 is configured to command arobotic actuator 116 that is capable of manipulating article of dishware110 via a mechanical coupling 118. Examples of robotic actuator 116include a multi degree of freedom robotic arm, a gantry robot, a multidegree of freedom linear stage assembly, and so on. Examples ofmechanical coupling 118 include grippers, vices, clamps, and othersimilar gripping mechanisms. Robotic actuator 116 is configured to, forexample, change a pose or a physical orientation of article of dishware110 with respect to imaging system 104, so that different portions ofarticle of dishware 110 are presented in a field of view of imagingsystem 104. In particular embodiments, robotic actuator 116 changes thephysical orientation of article of dishware 110 responsive to inputsfrom processing system 106. Changing the physical orientation of articleof dishware 110 with respect to imaging system 104 allows differentportions of article of dishware 110 to be imaged by imaging system 104which, in turn, allows all of article of dishware 110 to be imaged overa series of multiple images of article of dishware 110 as captured byimaging system 104.

FIG. 1B is a schematic diagram depicting an embodiment of a staindetection system 119. In some embodiments, stain detection system 119includes a first light source 120, a second light source 122, through anMth light source 124. Each of first light source 120 through Mth lightsource 124 is configured to illuminate an article of dishware such asarticle of dishware 110. In some embodiments, each of first light source120 through Mth light source 124 is configured to output light of aspecific frequency, intensity and wavelength. In some embodiments, firstlight source 120 through Mth light source 124 may illuminate article ofdishware 110 simultaneously. In other embodiments, some combination offirst light source 120 through Mth light source 124 may be used toilluminate article of dishware 110.

In some embodiments, a set of imaging systems comprising a first imagingsystem 126, a second imaging system 128, through an Nth imaging system130 are each configured to capture one or more images of lightreflecting off of article of dishware 110, where the light reflectingoff of article of dishware is generated by some combination of firstlight source 120 through Mth light source 124. One or more opticalfilters from a set of optical filters comprising an optical filter 132,an optical filter 134, through an optical filter 136 may be opticallycoupled to one or more of first light source 120 through Mth lightsource 124, or one or more of first imaging system 126 through Nthimaging system 130. Optical filter 132 through optical filter 136 areeach configured to modify light passing through them, and may be usedalong an incident light path from first light source 120 through Mthlight source 124 to article of dishware 110 (light path not shown), oralong a reflected light path from article of dishware 110 to firstimaging system 126 through Nth imaging system 130 (light path notshown). In some embodiments, optical filter 132 through optical filter136 may be mechanically switchable in a sense that a filter may bephysically placed in front of a light source or an imaging system forone set of images, and removed for another set of images by a mechanicalactuation system that may include one or more robotic actuators ormechanical actuators (not shown) that are similar to robotic actuator116.

In some embodiments, one subset of lights from a light set comprisingfirst light source 120 through Mth light source 124 may be used tocapture a first set of images by first imaging system 126 through Nthimaging system 130, while a second subset of lights from the light setcomprising first light source 120 through Mth light source 124 may beused to capture a second set of images by first imaging system 126through Nth imaging system 130, and so on. This process yields severalsets of images where article of dishware is illuminated by differentcombinations of light sources, i.e., by light having differentcharacteristics. This feature enables processing system 106 to implementdifferent analysis techniques for dirt detection or defect detection asdescribed herein.

In some embodiments, the sets of images captured by first imaging system126 through Nth imaging system 130 are received by processing system 106that is configured to process the one or more images to determine (ordetect) a presence of a stain or a defect on article of dishware 110.Methods used by processing system 106 to accomplish stain detection ordefect detection on article of dishware 110 are described herein.

In some embodiments, stain detection system 119 also includes roboticactuator 116 that can manipulate article of dishware 110 via mechanicalcoupling 118. Robotic actuator 116 is configured to, for example, changea pose or a physical orientation of article of dishware 110 with respectto first imaging system 126 through Nth imaging system 130, so thatdifferent portions of article of dishware 110 are presented inrespective fields of view of first imaging system 126 through Nthimaging system 130. In particular embodiments, robotic actuator 116changes the physical orientation of article of dishware 110 responsiveto inputs from processing system 106. Changing the physical orientationof article of dishware 110 with respect to first imaging system 126through Nth imaging system 130 allows different portions of article ofdishware 110 to be imaged by first imaging system 126 through Nthimaging system 130 which, in turn, allows all of article of dishware 110to be imaged over a series of multiple images of article of dishware 110as captured by first imaging system 126 through Nth imaging system 130.

Embodiments of stain detection systems such as stain detection system100 and stain detection system 119 described herein may use a singlelight source or multiple light sources combined with or more imagingsystems, both of which may be optically coupled to mechanically-actuatedoptical filters. In some embodiments, the light sources, imagingsystems, and optical filters are enclosed in a box to more tightlycontrol image acquisition conditions such as lighting. Thisconfiguration of light sources, imaging systems, and optical filtersenclosed in a box may be referred to as a “sensor.” In some embodiments,articles of dishware are presented to the imaging systems by roboticactuators, and the imaging systems and the lights are on a mountreachable by the robotic actuators. The light sources can be anycombination of visible lights, white lights, ultraviolet (UV) lights,and infrared (IR) lights to generate signals for a wide range dirt,soils, stains, and defects that may be encountered on an article ofdishware. In some embodiments, the imaging systems (such as imagingsystem 104, first imaging system 126 through Nth imaging system 130, andother imaging systems described herein) can be any combination of normalRGB cameras (featured with high resolution and high dynamic range), UVcameras, IR cameras, and hyperspectral cameras for capturing diversesignal types to improve sensitivity and detectability of the signals.

Other embodiments of a sensor include UV lights and visible white lightscombined with two cameras or more. Such embodiments utilize reflectancefrom food soils under visible white lights and fluorescence from foodsoils or stains under UV lights. Visible light is a broadband whitesource, and its color temperature ranges from 5000 K to 8300 K.Wavelengths of UV light can include 365 nm or 395 nm wavelengths. Whenfood soils are illuminated with UV lights, they fluoresce and generatelight signals residing in the visible band. The exact wavelengths ofsuch fluorescence signals depend on the food soil types. Since the UVemission is used only for exciting the food soils, the UV reflectancefrom the food soils are eliminated or heavily suppressed by a long-passfilter whose low cutoff frequency is, for example, 470 nm. In someembodiments, two white light sources complement each other in their foodsoil detectability. Since a vast majority of articles of dishware arewhite and specular, any (light) signals reflected off of food soilsunder visible white lights could often be hardly distinguishable fromthe signals reflected from the surface of such an article of dishware(also referred to as a “dish”). For such food soils, the UV fluorescencehas much higher contrast and significantly lower reflectance, givinghighly improved signal detectability or discernibility. On the contrary,some food soils such as dried coffee residue hardly produce fluorescenceunder UV. However, their colors under a visible white light are verydistinct from the dish surface, leading to enhanced discernibility ofthe food soils in the images.

In one embodiment, cameras and lights can be augmented with differenttypes of filters. The filters on the cameras can be made mechanicallyswitchable between different types to capture the specified wavelengthsof signals that are more suitable for the associated light sources. Forexample, UV fluorescence signals produced by 395 nm UV light mostlyreside in spectral bands beyond a certain wavelength (e.g., 490 nm). Insuch cases a long-pass UV filter that passes the signals in the spectralbands could noticeably improve visibility of any dirt or stains thatcreate those signals, and an associated the signal-to-noise ratios(SNRs). Another type of filter that improves detectability of dirt, soilor stains is a polarization filter. Since articles of dishware can bespecular and reflective, dirt signals are often washed out by suchreflections due to image saturation. Polarization filters can suppressthe reflections on the images if not completely removing them, therebyimproving the visibility and distinguishability of the dirt signals fromspecular reflections from the dish surface.

Another aspect of the stain detection systems described herein isrelated to presenting objects to the imaging systems (cameras). In someembodiments, a linearly-actuated conveyor moves an article of dishwareand places it in a field of view of an imaging system (camera) in aspecified position. In particular embodiments, multiple cameras can beplaced to create enough views that fully capture images associated withall the surfaces. Also, there can be a mechanism that flips the articleof dishware to image the back of the article of dishware. In anotherembodiment, an article of dishware can be presented to the camera by arobotic actuator. One merit of this design is its ability to presentarbitrary surfaces of an article of dishware in arbitrary orientationseven with a single camera. Another merit of this configuration is thatit can handle various lighting geometries by changing the orientationsof the dishware relative to the cameras and lights such that there areno spots that are too dark or too bright when considering all thecaptured images.

Some embodiments of the stain detection systems described herein usepolarization filters to improve the accuracy of human labeling of dirt,soil or stains on an article of dishware. The use of polarizationfilters has already been described herein as part of improving signalquality by removing reflections. When reflections cannot be completelyeliminated, such specular reflections can be confused with bright dirtspots on the image. In such cases, multiple captures acquired withvarying polarization filter configurations can be used to improve theaccuracy of human labeling during an associated system training process.The varying configurations of the filters create the specularreflections on different parts of the dish image, while the dirt signalsstay in the same spots; hence the dirt signals can successfully beseparated for more accurate labeling. Another embodiment may use highdynamic range (HDR) cameras to suppress or eliminate the specularreflections.

The UV lights associated with the stain detection systems describedherein can also help distinguish dirt, soils or stains on articles ofdishware from colorful patterns for data labeling. Under visible lights,patterns on dishes could be confused with the dirt, soils or stains. Forexample, cheese residues on plates can be confused with yellow dotpatterns and can make data labeling less accurate. The UV light usuallyresults in fluorescence only for the actual dirt, soils or stains, butnot the patterns on the dish. Using diverse light sources and theirassociated characteristics significantly extends the applicability ofdirt detection to numerous dish types with various textures andpatterns.

FIG. 2 is a block diagram depicting an embodiment of a processing system106 capable of operating a stain detection system configured to detect astain on an article of dishware. In some embodiments, processing system106 includes a communication manager 202 that is configured to managecommunication protocols and associated communication with externalperipheral devices as well as communication within other components inprocessing system 106. For example, communication manager 202 may beresponsible for generating and maintaining an interface betweenprocessing system 106 and imaging system 104. Communication manager 202may also manage communication between different components withinprocessing system 106.

In some embodiments, processing system 106 includes a memory 204 that isconfigured to store data associated with stain detection system 100.Data stored in memory 204 may be temporary data or permanent data. Insome embodiments, memory 204 may be implemented using any combination ofhard drives, random access memory, read-only memory, flash memory, andso on. In particular embodiments, data stored in memory 204 may includepositioning data associated with robotic actuator 116, data from imagingsystem 104, and so on.

Processing system 106 may also include an imaging system interface 206that is configured to interface processing system 106 with one or moreimaging systems such as imaging system 104. In some embodiments, imagingsystem interface 206 may include any combination of connectivityprotocols such as IEEE 1394 (FireWire), Universal Serial Bus (USB), andso on. Imaging system interface 206 allows processing system 106 toreceive images from an associated imaging system, while also sendingcommands to the imaging system (e.g., a command to capture an image whenan article of dishware is in a particular position in a field of view ofthe imaging system).

Processing system 106 may also include a processor 208 that may beconfigured to perform functions that may include generalized processingfunctions, arithmetic functions, and so on. Processor 208 may also beconfigured to perform three-dimensional geometric calculations and solvenavigation equations in order to determine relative positions,trajectories, and other motion-related and position-related parametersassociated with manipulating an object by a robotic actuator.

In some embodiments, processing system 106 includes a robotic actuatorcontroller 210 that is configured to output actuation commands torobotic actuator 116. The commands output by robotic actuator controller210 may include positioning commands, object gripping commands, objectrelease commands, object repositioning commands, and so on.

A user interface 212 may be included in processing system 106. In someembodiments, user interface 212 is configured to receive commands from auser or display information to the user. For example, commands receivedfrom a user may be basic on/off commands, and may include variableoperational speeds. Information displayed to a user by user interface212 may include, for example, system health information and diagnostics.User interface 212 may include interfaces to one or more switches orpush buttons, and may also include interfaces to touch-sensitive displayscreens. In some embodiments, user interface 212 may allow a user toannotate an image of an article of dishware as described herein. Thisannotation may be performed, for example, via a touchscreen, or acombination of a keyboard/mouse interface and a visual display monitor.

In some embodiments, processing system 106 includes an image analysissystem 214 that is configured to process images of an article ofdishware captured by, for example, imaging system 104 to determine apresence of a stain, soil or dirt on an article of dishware. Imageanalysis system 214 may include subsystems that implement convolutionalneural network (CNN) based algorithms for processing the images, asdiscussed herein. A data bus 216 interconnects all subsystems associatedwith processing system 106, transferring data and commands withinprocessing system 106.

FIG. 3 is a block diagram depicting an embodiment of image analysissystem 214. In some embodiments, image analysis system 214 is configuredto process one or more images associated with an article of dishware andresponsive to the associated image analysis, to determine whether anarticle of dishware is soiled, stained, dirty or damaged, and determinea location of the soil, stain, dirt or defect on the article ofdishware. In some embodiments, image analysis system 214 includes neuralnetworks 302 that are configured to implement deep learning techniqueson images received from imaging system 104 to determine a presence of astain, dirt, or a defect on an article of dishware. In particularembodiments, neural networks 302 are comprised of convolutional neuralnetworks (CNNs). In some embodiments, neural networks 302 may beconfigured to automatically extract any features and rules buried in alarge amount of labeled or annotated data (in this case, labeled orannotated visual data associated with one or more images associated withan article of dishware). This stipulates highly accurate data labelingand annotation for highly accurate computer-vision components, asdiscussed herein. The details on a choice of specific data labeling andannotation procedures, the CNN architectures, and their associatedtraining procedures are discussed herein.

In some embodiments, image analysis system 214 includes a cleanlinessclassifier 304 that is configured to classify a level of cleanlinessassociated with an article of dishware. In particular embodiments, thecleanliness classification is a binary classification that is comprisedof a dirty or not dirty classification associated with the article ofdishware, where a dirty classification is used to include a detection ofa presence by image analysis system 214 of a stain, dirt, a soil, or adefect on the article of dishware. In some embodiments, cleanlinessclassifier 304 may be configured to classify images of an article ofdishware prior to a wash. In other embodiments, cleanliness classifier304 may be configured to classify images of an article of dishware aftera wash to determine whether the article of dishware is still dirty. Instill other embodiments, cleanliness classifier 304 may be configured toclassify images of an article of dishware before and after washing.

Some embodiments of image analysis system 214 include a cleanlinessquantifier 306 that is configured to produce a scale of cleanliness(i.e., a cleanliness quantification) in terms of a number of discretelabels based on images of an article of dishware captured before orafter washing. In some embodiments, cleanliness quantifier 306 may beimplemented using a CNN, where cleanliness quantification is achieved byfirst training the CNN by having a human label each image or a set ofimages based on the different levels of cleanliness during a trainingprocess for an associated CNN. For example, an image may be labeled asvery dirty, mildly dirty, almost clean, and clean. A large number oflabeled images from each class are used to train the CNN. The trainedCNN automatically learns about features from the vast amount of datathat can be used to tell that a plate is very dirty, mildly dirty,almost clean, and clean. Such features are stored in the form of weightsin the CNN. During prediction time (i.e., when the CNN is deployed toimplement its functionality), an image or a set of images of an articleof dishware are input to the CNN and the CNN outputs a classificationlabel.

In some embodiments, cleanliness classifier 304 or cleanlinessquantifier 306 may be implemented using adaptations of a ResidualNetwork (ResNet-50) CNN. In particular embodiments, an associatedtraining algorithm for a ResNet-50 CNN is a stochastic gradientoptimization algorithm. An associated learning rate for the stochasticgradient descent is selected by sweeping a range of feasible numbers andby selecting the one that produces the best results.

In some embodiments, image analysis system 214 includes a dirt localizer308 that is configured to estimate locations of one or more boundingboxes that tightly enclose a corresponding plurality of spots in animage associated with an article of dishware, with a spot beingassociated with dirt, a soil, a stain, or a defect. This process isreferred to as dirt localization. In some embodiments, these boundingboxes may also be referred to as tight bounding boxes. In particularembodiments, dirt localizer 308 may be implemented using a CNN. During aCNN training phase, a user manually annotates any locations of spots (orartifacts in an image corresponding to soil, dirt, stains, defects andso on) using tight bounding boxes, and this human input is used tocharacterize the spots. A location of a tight bounding box in an imageis represented by a set of four numbers: the center x coordinate, thecenter y coordinate, the width of the tight bounding box, and the heightof the tight bounding box. The locations of all tight bounding boxes inan image and a set of all associated images are then fed to a CNN astraining data. In some embodiments, a region-based CNN (Faster R-CNN) isthen trained to learn any features associated with the spots. A FasterR-CNN may be configured to classify the locations of the tight boundingboxes for dirty spots associated with the input images from a massiveamount of training data. The outputs of the Faster R-CNN are theestimated locations of the tight bounding boxes for the dirty spots inan input image on which the prediction is performed. A regression(regressive analysis) is performed to produce an estimated location ofthe tight bounding boxes for dirty spots. A level of dirtiness of eachdirty spot in each tight bounding box can also be classified in asimilar manner to that described in the cleanliness quantificationprocess associated with cleanliness quantifier 306. In this case, thetraining data would be a set of images, for each of which image thereare multiple locations of the tight bounding boxes for dirty spots andlabels to indicate a level of dirtiness for each bounding box. In theprediction step in the deployed system, the Faster R-CNN then estimatesthe locations of the bounding boxes for the spots for each input image,and each spot is assigned a label indicating the degree of dirtiness.The training is done by following an alternating 4-step optimizationthat is associated with a Faster R-CNN to perform the end-to-endtraining. The alternating 4-step optimization, also referred to a 4-stepalternating training process, is composed of 1) training a regionproposal network (RPN) that proposes candidates (i.e., proposed regions)for bounding boxes, which is initialized with a pre-trained model andthen fine-tuned end-to-end, 2) using the proposed regions andinitializing a detection network with the pre-trained model, 3) usingany numerical weights trained in 2) to initialize the RPN, fixing sharedconvolutional layers, and fine-tuning the shared convolutional layersthat belong to only the RPN, and 4) fixing the shared convolutionallayers shared between the detection network and the RPN and fine tuningthe shared convolutional layers that belong to only the detectionnetwork. In some embodiments, the detection network is configured toadjust and further tighten any tight bounding boxes proposed by the RPN.

In some embodiments, image analysis system 214 may include a freshnessestimator 310 that is configured to determine whether a food stain orsoil on an article of dishware is fresh, or determine an approximate ageof the food stain. This functionality provided by freshness estimator310 could be useful particularly in such places as cafeterias in ahospital or schools where people drop off a tray of dishwares they eatoff of. In such cases, food soil freshness for a purpose of dishwashingcan be timed from the time where the diners drop off the trays.Alternatively, food soil freshness for a particular food soil can beindirectly estimated by measuring a time taken for a dishwashing machineto remove the food soil. For each dirty article of dishware, imagery andassociated image processing may be used to determine when the article ofdishware is completely cleaned by a dishwashing machine. The time takenfor the article of dishware to be completely cleaned can then be used asan approximate measure of freshness of a soil or stain. This time can bemeasured in, for example, increments of 0.2 s to estimate the degree offreshness of the food soils at a sufficient resolution. Such discretetime steps can be used as labels for the images captured before thearticles of dishware are cleaned. These images and the associated labels(i.e., the cleaning times) can then be used as the training data totrain a CNN for classification of the time labels. This allows anassociated automated dishwashing system to estimate how long it wouldtake to clean a stain that has been successfully characterized. In someembodiments, the CNN architecture used is a modification of ResNet-50,which is trained with a stochastic gradient descent algorithm.

Some embodiments of image analysis system 214 include a soil typeclassifier 312 that is configured to estimate what type of menus or foodingredients are in the associated with a particular soil, stain or dirton an article of dishware. This functionality may incorporate priorknowledge of possible food items that may be placed in or on the articleof dishware, such as a menu of a restaurant or a business that runs adishwashing system that includes the stain detection systems discussedherein.

A dirt segmentation system 314 may be included in some embodiments ofimage analysis system 214. Dirt segmentation system 314 is configured toproduce contours that tightly enclose any stains or spots on an articleof dishware. In some embodiments, dirt segmentation system 314 may beimplemented using a CNN-based architecture similar to the other CNNarchitectures discussed herein. Dirt segmentation system 314 may besubject to training and deployment steps as discussed above for othersubsystems of image analysis system 214. In some embodiments, a user maydesignate tight-enclosing contours around a spot, stain or defect duringa CNN training phase. Such training methods are performed over a largeset of data, after which the CNN-based dirt segmentation system 314 maybe deployed with full functionality.

In some embodiments, image analysis system 214 includes a dish damageidentifier 316 that is configured to detect or determine a presence of adefect or damage on an article of dishware. Examples of defects ordamage include chips, cracks, and scratches. Dish damage identifier 316estimates the existence of damage, possibly with their associatedpositions on articles of dishware, using the techniques associated withdirt and stain detection, including a training phase and a deploymentphase as discussed earlier.

In some embodiments, functionalities of any combination of cleanlinessclassifier 304, cleanliness quantifier 306, dirt localizer 308,freshness estimator 310, soil type classifier 312, dirt segmentationsystem 314, and dish damage identifier 316 may be each be implementedusing a CNN-based architecture such as a ResNet-50, Faster R-CNN, and soon. Each CNN is configured to operate in two phases—a training phase anda deployment phase. During a training phase, a CNN is “taught” by, forexample, a user who manually annotates and classifies associated regionsof interest in a large dataset of images corresponding to articles ofdishware. The CNN is then deployed to be functional, where the CNN usesthe trained data to intelligently detect and classify a spot, a stain,dirt, or a defect on an article of dishware.

In some embodiments, images acquired after washing an article ofdishware by a dishwasher (dishwashing machine) are processed by CNNs toproduce a binary classification output on a cleanliness associated withthe article of dishware, or a multi-class classification outputrepresenting a level of the cleanliness. In some embodiments, a binaryclassification result may be used to provide quality assurance to usersof the dishwashing machine. A data labeling and training proceduresimilar to the one described above for binary and multi-classclassification can also be used for quality assurance associated with adishwashing process. Images taken after any cleaning steps are completedare labeled by human users as a part of a training phase to indicatewhether each article of dishware in an image is completely clean or not;hence a binary designation on the output. The images and the labels arethen used to train an adaptation of a ResNet-50 CNN similar to the onedescribed above. For finer granularity for cleanliness, the human usersmay label the same image in a finer-grained manner: very dirty, mildlydirty, almost clean, and clean. The remaining steps for training theResNet-50 CNN are similar, except that the output is a multi-classlabel, and an associated loss function that is minimized by thestochastic gradient descent is a categorical cross-entropy. A trainedCNN learns in the form of weights that are used as filters to derive thefeatures that are automatically learned from a massive amount of data.This multi-class classification result may be used to show the cleaningefficacy trend to the users for monitoring the machine performance overtime or during a day's operation.

In some embodiments, the images confirmed to be clean can be classifiedto inspect scratches, chips, defects, and damages on the clean dishes.These results can be used to inform a status of articles of dishware,and to recommend replacing any damaged articles of dishware. For defectdetection, each image is labeled or annotated by a human user either inthe form of binary classification or multi-class classification during aCNN training phase. For binary classification, humans label each imageto indicate whether the dishware in the image has any defects or damage.For defect or damage detection, humans annotate the locations of defectsand damage in the form of bounding boxes tightly enclosing the each ofthe defects and damage locations. Also, each bounding box can be labeledto indicate the type of damages such as chips, cracks, etc. For traininga CNN for classification, a ResNet-50 can be adapted in a manner similarto the procedure described above. For defect detection, as in dirtlocalization, a Faster R-CNN can be modified to perform the defectdetection task.

In some embodiments, dirt segmentation can be performed in a mannersimilar to dirt localization. Dirt localization takes the tight boundingboxes discussed earlier, enclosing a soil as inputs to the CNN forconducting the dirt localization tasks. In some embodiments, a user mayuse a contour or polygon to manually tightly enclose a food soil orstain. The dirt segmentation takes such polygons tightly enclosing foodsoils as inputs along with the associated images. The polygons arealigned with the boundaries of the food soils. Such polygons areannotated by humans for creating the labeled data. In an implementation,a polygon may be equivalently represented as a set of pixels that areenclosed by the polygon. The inputs, the images and the polygonannotations are used to train an adaptation of a Mask R-CNN. During theprediction (deployment) phase, an image is passed through the adaptationof a Mask R-CNN to produce a set of pixels that are predicted as foodsoils.

Any images of an article of dishware acquired before washing areannotated, where a human user manually draws bounding boxes or contoursaround any dirt, stains, soils or defects. An object detection methodbased on CNNs processes these images to estimate the respectivelocations of the dirt, stains, soils or defects in the form of boundingboxes. These bounding boxes can be used to configure an automateddishwashing machine. Based on the results, the dishwashing machine isconfigured such that a water nozzle or spray associated with thedishwasher for washing the article of dishware can be focused moreheavily on those dirt areas for more effectively cleaning of thoseareas, as discussed herein. This mechanism may be augmented by adding afeedback loop based on the cleanliness classification of those dirtspots while the article of dishware is washed. A pressure of the wateror cleaning solution may also be adjusted by the feedback that canindicate the amount of dirt, stains, or soils left. The amount of dirt,stains or soils can be estimated by cleanliness quantifier 306, or dirtsegmentation system 314 can be used to estimate a surface areaassociated with the dirt, stains or soils.

FIG. 4 is a schematic diagram depicting an embodiment of a targetedcleaning system 400. In some embodiments, targeted cleaning system 400is configured to combine the stain detection systems and methodsdescribed above with a dishwashing effort associated with an article ofdishware in real time. In some embodiments, targeted cleaning system 400includes a first imaging system 416 and a second imaging system 418,each of which are configured to capture one or more images of an articleof dishware 420. In some embodiments, first imaging system 416 andsecond imaging system 418 are configured to capture images of differentportions of article of dishware 420. The one or more images captured byeach of imaging system 416 and imaging system 418 are received byprocessing system 106 that is configured to detect a presence andlocation of a stain, soil, or dirt on article of dishware 420, using themethods described herein (e.g., a CNN-based dirt detection method).Processing system 106 is also configured to issue commands to asteerable nozzle 410 that is configured to spray a fluid 422 (e.g.,dishwashing solution) at a specified temperature, pressure andconcentration, onto article of dishware 420. In some embodiments, ifwater pressure of a typical house is used, the pressure of fluid 422 maybe varied between 10 and 100 psi. In other embodiments, if a highpressure pump is used for the dishwashing process, the pressure of fluid422 may be adjusted from 50 to 2500 psi. In some embodiments, thetemperature of fluid 422 may be varied between 100 F and 180 F.

In some embodiments, steerable nozzle 410 receives fluid 422 via a fluidfeed 414 from a fluid source (not shown in FIG. 4). By spraying fluid422 onto article of dishware 420, steerable nozzle 410 performs afunction of dishwashing, where article of dishware 420 is washed andcleaned by fluid 422 sprayed by steerable nozzle 420.

In some embodiments, steerable nozzle 410 is mounted on a gantry 408. Insome embodiments, gantry 408 may be an X-Y gantry. In particularembodiments, gantry 408 is attached to a rigid support 402 via a firstmounting 404 and a second mounting 406. Examples of rigid support 402include a workspace, a platform, or any other rigid support that issecurely attached to a wall, ceiling or floor of an operatingenvironment associated with targeted cleaning system 400. In someembodiments, steerable nozzle 410 is mounted on gantry 408 such thatarticle of dishware 410 is conveyed under gantry 408 and steerablenozzle 410 by, for example, a conveyor belt (not shown in FIG. 4). Inother embodiments, steerable nozzle 410 may be implemented by mounting afixed nozzle on a multi degree of freedom robot end effector. In stillother embodiments, steerable nozzle 410 may be fixed while targeting astain is accomplished by movement of the article of dishware gripped bya robotic actuator and moved in front of steerable nozzle 410. Ingeneral, steerable nozzle 410 is configured to move relative to articleof dishware 420, either by configuring steerable nozzle 410 to be amoveable nozzle, or configuring a robotic actuator to move article ofdishware 420 relative to steerable nozzle 410, or both.

Responsive to commands from processing system 106, steerable nozzle 410is configured to move along gantry 408 in directions parallel to alength and a width of article of dishware 420; gantry 408 provides achannel along which steerable nozzle 410 may be moved in thesedirections. For example, FIG. 4 shows a direction of motion 412 ofsteerable nozzle 410 that is parallel to a length of article of dishware420. In some embodiments, steerable nozzle 410 is configured to pivotabout at least one axis associated with steerable nozzle 410. Thispivoting feature allows steerable nozzle 410 to spray fluid 422 ontoarticle of dishware 420 at different angles. A motion of steerablenozzle 410 along gantry 408 or about the at least one axis may beachieved by mechanically configuring steerable nozzle 410 to be drivenby, for example, electric motors such as stepper motors orservomechanisms.

In some embodiments, targeted cleaning system 400 may also include oneor more light sources (not shown in FIG. 4) that are configured toilluminate article of dishware 420 to assist first imaging system 416and second imaging system 418 in capturing the at least one image ofarticle of dishware 420. These light sources may be any combination ofwhite light sources, UV light sources, or any other light source asdescribed herein.

In some embodiments, first imaging system 416 and second imaging system418 may capture at least one image of article of dishware 420 prior toarticle of dishware 420 being washed by steerable nozzle 410. Processingsystem 106 processes the at least one image received from first imagingsystem 416 and second imaging system 418 to determine a presence andlocation of a stain, dirt, or soil on article of dishware 420 usingdifferent components of image analysis system 214 such as cleanlinessclassifier 304, cleanliness quantifier 306, and dirt localizer 308.Responsive to determining a presence and location of a stain, dirt, orsoil on article of dishware 420, processing system 106 commandssteerable nozzle 410 to move along gantry 408 and pivot about the atleast one axis so that a cleaning (dishwashing) action of steerablenozzle 410 is focused on the location of the stain, dirt, or soil. Forexample, steerable nozzle 410 may initially spray the whole surface ofarticle of dishware 410 to achieve a general cleaning action. Thisspraying action may be at a predetermined, baseline temperature,pressure and concentration associated with fluid 422. Next, steerablenozzle 410 may be commanded by processing system to focus spraying fluid422 to a local region on article of dishware 420 that is associated withthe location of the stain, dirt or soil. A temperature and pressure offluid 422 may be increased relative to the baseline temperature andpressure, while a concentration of a cleaning agent (e.g., dishwashingsoap) in fluid 422 may be increased relative to the baselineconcentration. This technique, also referred to as targeted cleaning, isan intelligent dishwashing technique, where concentrated cleaningefforts are focused (or targeted) only in localized regions on articleof dishware 420 where stains, dirt, or soils are detected using thesystems and methods described herein. In some embodiments, outputs fromsoil type classifier 312 may be used to characterize a nature of a soilon article of dishware 420. For example, a soil associated with freshcoffee stains may require less cleaning effort than baked-on cheese.This targeted cleaning approach allows for an increase in a throughputassociated with the dishwashing process, and a reduction in resourcesneeded to clean the article of dishware relative to, for example, ageneralized cleaning system that sprays the entire article of dishwareusing a high temperature, high pressure, high concentration dishwashingsolution.

In some embodiments, if processing system 106 detects and localizesmultiple regions of stains, dirt, or soils on article of dishware 420,the targeted cleaning method using steerable nozzle 410 can be appliedsequentially to each region. First imaging system 416 and second imagingsystem 418 may be configured to capture images of article of dishware420 at different times (e.g., periodically) during the cleaning (i.e.,dishwashing) process, between subsequent cleaning phases. Each set ofsuch images may be analyzed by image analysis system 214 associated withprocessing system 106 to determine whether the stains, dirt, or soilsdetected prior to beginning the cleaning process have been cleaned, andwhether article of dishware 420 is clean. If article of dishware 420 isstill dirty, processing system continues to command steerable nozzle 410to perform targeted cleaning on the regions on article of dishware 420that are still dirty. Once article of dishware 420 is determined to beclean, article of dishware 420 is conveyed away from steerable nozzle410 to make way for the next article of dishware to be processed.

FIG. 5A is a schematic diagram depicting an example of data labeling andannotation associated with operation of an embodiment of a staindetection system. FIG. 5A depicts an image 500 of an article of dishware502. Image 500 may be captured by, for example, imaging system 104. FIG.5A depicts a stain 506 on article of dishware 502, along with a tightbounding box 504 as described above. Tight bounding box 504 may beuser-defined during a training phase associated with a CNN, or generatedby the CNN during a deployment phase. Tight bounding box 504 essentiallydelineates a stain, dirt, or soil on article of dishware 502 for apurpose of classification.

FIG. 5B is a schematic diagram 508 depicting an example of data labelingand annotation associated with operation of an embodiment of a staindetection system. Specifically, FIG. 5B depicts an example of a binaryclassification as would be generated by cleanliness classifier 304. FIG.5B shows an image 510 of a clean article of dishware 512, and an image514 of a dirty article of dishware 516 with a stain 518. Image 510 andimage 514 may be captured separately by, for example, imaging system102. Cleanliness classifier 304 is configured to individually processeach of image 510 and image 514 and generate a binary classificationassociated with each of image 510 and image 514.

In some embodiments, a training mode of a CNN associated withcleanliness classifier 304, a user would label clean article of dishware512 as clean, and dirty article of dishware 516 as dirty. During adeployment phase, clean article of dishware 512 would be classified asclean by the CNN associated with cleanliness classifier 304; the sameCNN would classify dirty article of dishware 516 as dirty, consistentwith a binary dirty/not dirty paradigm.

FIG. 5C is a schematic diagram 520 depicting an example of data labelingand annotation associated with operation of an embodiment of a staindetection system. Specifically, FIG. 5C depicts an example of amulti-class classification as would be generated by cleanlinessquantifier 306. FIG. 5C shows a first image 522 of a clean article ofdishware 524, a second image 526 of an article of dishware 528 with afirst stain 530, a third image 532 of an article of dishware 534 with asecond stain 536, and a fourth image 538 of an article of dishware 540with a third stain 542. (The term “stain” is used to denote a stain,dirt, or a soil.) For multi-class classification (i.e., cleanlinessquantification), article of dishware 524 would be classified as clean,article of dishware 528 would be classified as almost clean, article ofdishware 534 would be classified as moderately dirty, and article ofdishware 540 would be classified as very dirty. These classificationsare consistent with the size of each stain—article of dishware 524 hasno stain; first stain 530 is smaller than second stain 536, which issmaller than third stain 542.

In some embodiments, the above multi-class classification is performedby a human user during a CNN training phase, where a large number ofimages are classified and used to build the CNN's intelligence base. Insome embodiments, the number of images used may range from a fewthousand to hundreds of thousands to millions. During a deploymentphase, the CNN utilizes this intelligence base to multi-class classifyan article of dishware based on one or more images received by anassociated imaging system.

FIG. 5D is a schematic diagram depicting an example of data labeling andannotation associated with operation of an embodiment of a staindetection system. Specifically, FIG. 5D depicts an example of dirtlocalization as performed by dirt localizer 308. FIG. 5D shows an image544 of an article of dishware 546 with a first stain 550 and a secondstain 554. First stain 550 is shown to be enclosed by a tight boundingbox 548, while second stain is shown to be enclosed by a tight boundingbox 552. Tight bounding box 548 and tight bounding box 552 serve tolocalize (i.e., provide a location of) each of first stain 550 andsecond stain 554 on article of dishware 546.

In some embodiments, tight bounding box 548 and tight bounding box 552may be generated by a human user during a CNN training phase, orautomatically by the CNN during a deployment phase. Tight bounding box548 and tight bounding box 552 serve to provide specific locations ofthe associated stains to a targeted cleaning system such as targetedcleaning system 400 so that focused (i.e., targeted) cleaning may beperformed at the location of the associated stains as discussed above.

FIG. 5E is a schematic diagram depicting an example of data labeling andannotation associated with operation of an embodiment of a staindetection system. Specifically, FIG. 5E depicts a dirt segmentationoutput that may be generated by dirt segmentation system 314. FIG. 5Eshows an image 556 of an article of dishware 558 with a stain 562. Apolygon 560 (also referred to as a contour) is drawn around stain 562 totightly enclose stain 562.

In some embodiments, polygon 560 may be drawn manually by a human userduring a training phase of a CNN associated with dirt segmentationsystem 314. Polygon 560 may also be drawn automatically by the CNNduring a deployment phase.

FIG. 6 is a flow diagram depicting an embodiment of a method 600 todetermine whether an article of dishware is dirty. At 602, a lightsource such as light source 102 illuminates an article of dishware suchas article of dishware 110. At 604, an imaging system such as imagingsystem 104 captures at least one image of the article of dishware. Next,at 606, a processing system such as processing system 106 processes theat least one image to determine a presence of a stain on the article ofdishware. In some embodiments, processing system 106 performs processingusing the CNN-based systems and methods described herein. At 608, themethod checks to see if a stain (i.e., a stain, dirt or a soil) isdetected on the article of dishware. If a stain is detected, the methodgoes to 614, where the article of dishware is labeled as dirty. If, at608, a stain is not detected, then the method goes to 610, where themethod checks to see if the entire article of dishware is imaged.

In some embodiments, checking whether the entire article of dishware isimaged may be done, for example, by a priori storing dimensions of thearticle of dishware as captured in an image, or storing dimensions ofdifferent portions of the article of dishware as captured in multipleimages by the imaging system. These images serve as reference images foran article of dishware; different articles of dishware with differentdimensions may be imaged in this way to be associated with uniquereference image sets. By comparing the image or images corresponding atruntime to these reference images, an article of dishware, whetherimaged in its entirety or in portions, can be analyzed to determinewhether the entire article of dishware has been imaged.

In other embodiments, an open-loop method can be implemented where, ifan article of dishware is recognized in advance and the article ofdishware is presented to the imaging system in a fixed way by a roboticactuator, then spatial positions of the robotic actuator correspondingto different portions of the article of dishware as presented to theimaging system can be preloaded into the processing system in the formof a data stack. The robotic actuator is then moved through each spatialposition and a corresponding image is captured by the imaging system foreach spatial position. This allows the processing system to keep trackof whether the entire article of dishware has been imaged.

At 610, if the method determines that the entire article of dishware isimaged, then the method goes to 616, where the article of dishware islabeled as clean. At 610, if the method determines that the entirearticle of dishware is not imaged then the method goes to 612, where arobotic actuator such as robotic actuator 116 changes a pose of thearticle of dishware as viewed by the imaging system. Changing a pose ofthe article of dishware as viewed by the imaging system by roboticactuator 116 is consistent with imaging different portions of thearticle of dishware as described in the discussion of step 610. Themethod then returns to 604, where the process continues till the articleof dishware is labeled either as clean or dirty.

FIG. 7 is a flow diagram depicting an embodiment of a method 700 todetermine whether an article of dishware is defective. At 702, a lightsource such as light source 102 illuminates an article of dishware suchas article of dishware 110. At 704, an imaging system such as imagingsystem 104 captures at least one image of the article of dishware. Next,at 706, a processing system such as processing system 106 processes theat least one image to determine a presence of a defect on the article ofdishware, where a defect may include a scratch, a chip or some otherdamage. In some embodiments, processing system 106 performs processingusing the CNN-based systems and methods described herein. At 708, themethod checks to see if a defect is detected on the article of dishware.If a defect is detected, the method goes to 714, where the article ofdishware is labeled as defective. If, at 708, a defect is not detected,then the method goes to 710, where the method checks to see if theentire article of dishware is imaged using the techniques describedabove.

At 710, if the method determines that the entire article of dishware isimaged, then the method goes to 716, where the article of dishware islabeled as non-defective. At 710, if the method determines that theentire article of dishware is not imaged then the method goes to 712,where a robotic actuator such as robotic actuator 116 changes a pose ofthe article of dishware as viewed by the imaging system. Changing a poseof the article of dishware as viewed by the imaging system by roboticactuator 116 is consistent with imaging different portions of thearticle of dishware as described in the discussion of step 610. Themethod then returns to 704, where the process continues till the articleof dishware is labeled either as defective or non-defective.

FIG. 8 is a flow diagram depicting an embodiment of a method 800 totrain a neural network to perform a function of stain detection. At 802,an article of dishware such as article of dishware 110 is illuminated bya light source such as light source 102. At 804, an imaging system suchas imaging system 104 captures a first image of the article of dishwareusing a first polarizing filter. At 806, the imaging system captures asecond image of the article of dishware using a second polarizingfilter. At 808, the imaging system captures a third image of the articleof dishware using a third polarizing filter. As discussed herein, usingmultiple polarizing filters (or polarization filters in differentconfigurations) allows the system to prevent specular reflections off ofthe article of dishware from adversely affecting the results. Ifnecessary, additional polarization filters or additional polarizationfilter configurations may be used to capture additional images of thearticle of dishware.

At 810, a processing system such as processing system 106 processes thefirst image, the second image and the third image using the systems andmethods described herein, and enables a user to annotate visual dataassociated with the first image, the second image and the third image.In some embodiments, this annotation may include a user drawing tightbounding boxes around dirt, stains, soils or defects, or delineatingregions on the article of dishware using contours or polygons asdescribed herein. In some embodiments, a user may interact withprocessing system via user interface 212. At 812, the annotated data isused to train a neural network such as one or more of the CNNs describedherein. Next, at 814, the method checks to determine whether avalidation accuracy of the neural network is above a threshold value. Insome embodiments, the threshold value is defined to be approximately 85%accuracy, corresponding approximately to an accuracy associated withhuman performance (i.e., an accuracy that would be achieved if a humanperformed a stain detection function). The neural network may also beconfigured to operate at higher accuracy levels (e.g., 90%). Step 814aims to determine whether the neural network is sufficiently trained toproduce reliable and accurate results. If the validation accuracy of theneural network is above the threshold value, then the method goes to816, where the neural network outputs a network model that is used forruntime deployment. If, at 814, the validation accuracy of the neuralnetwork is not above the threshold value, then the method returns to802, and the process repeats till the validation accuracy of the neuralnetwork is above the threshold value.

FIG. 9 is a flow diagram depicting an embodiment of a method 900 thatuses a trained neural network to perform a function of stain detectionand present a level of dirtiness as an output classification. At 902, anarticle of dishware such as article of dishware 110 is illuminated by alight source such as light source 102. At 904, an imaging system such asimaging system 104 captures a first image of the article of dishwareusing a first polarizing filter. At 906, the imaging system captures asecond image of the article of dishware using a second polarizingfilter. At 908, the imaging system captures a third image of the articleof dishware using a third polarizing filter. At 910, a processing systemsuch as processing system 106 processes the first image, the secondimage and the third image via a network model associated with a neuralnetwork such as a CNN. At 912, the neural network classifies a level ofdirtiness associated with the article of dishware. Finally, at 914, theneural network presents the level of dirtiness as an outputclassification. In some embodiments, steps 912 and 914 may be performedby a combination of cleanliness classifier 304 and cleanlinessquantifier 306, each of which may use a neural network to implement therelevant processing functions.

FIG. 10 is a flow diagram depicting an embodiment of a method 1000 thatuses a trained neural network to perform a function of stain detectionand generate a dirt segmentation output. At 1002, an article of dishwaresuch as article of dishware 110 is illuminated by a light source such aslight source 102. At 1004, an imaging system such as imaging system 104captures a first image of the article of dishware using a firstpolarizing filter. At 1006, the imaging system captures a second imageof the article of dishware using a second polarizing filter. At 1008,the imaging system captures a third image of the article of dishwareusing a third polarizing filter. At 1010, a processing system such asprocessing system 106 processes the first image, the second image andthe third image via a network model associated with a neural networksuch as a CNN. At 1012, the neural network generates a bounding boxaround each stain detected by the neural network. Finally, at 1014, theneural network generates a dirt segmentation output. In someembodiments, steps 1012 and 1014 may be performed by a combination ofdirt localizer 308 and dirt segmentation system 314, each of which mayuse a neural network to implement the relevant processing functions.

FIG. 11 is a schematic diagram depicting an embodiment of arecirculation system 1100 associated with articles of dishware. In someembodiments, recirculation system 1100 includes a first imaging system1128 and a second imaging system 1129 that are each configured tocapture one or more images of an article of dishware. In someembodiments, first imaging system 1128 is configured to capture the oneor more images before the article of dishware is washed by, for example,an automated dishwashing system. In particular embodiments, secondimaging system 1129 is configured to capture the one or more imagesafter the article of dishware has been washed by the automateddishwashing system.

In some embodiments, each of imaging system 1128 and imaging system 1129is coupled to a processing system 1142 that is configured to communicatebi-directionally with each of imaging system 1128 and imaging system1129 via an electrical coupling. In particular embodiments, processingsystem 1142 is configured to receive images or imaging data from each ofimaging system 1128 and imaging system 1129, where the images or imagingdata include one or more images of an article of dishware as captured byimaging system 1128 or imaging system 1129. Processing system 1142 mayalso be configured to issue commands to each of imaging system 1128 andimaging system 1129 that include triggering imaging system 1128 andimaging system 1129 to capture an image, varying exposure settings oneach of imaging system 1128 and imaging system 1129, and so on. In someembodiments, imaging system 1128 and imaging system 1129 may include anycombination of RGB cameras, UV cameras, hyperspectral imaging cameras,high dynamic range (HDR) cameras, and so on.

In some embodiments, a robotic actuator 1122 is configured to pick up anarticle of dishware from a stack of soiled dishware 1102 resting on awork surface 1104. In particular embodiments, robotic actuator 1122 maypick up an article of dishware from stack of soiled dishware 1102 andpresent the article of dishware in a field of view of first imagingsystem 1128. In some embodiments, first imaging system 1128 isconfigured to capture one or more images of the article of dishware todetermine a presence and location of at least one stain or soil on thearticle of dishware. This set of one or more images can be referred toas a first set of images. In some embodiments, robotic actuator 1122 maymanipulate the article of dishware so as to change a pose of the articleof dishware in the field of view of first imaging system 1128. Thisallows first imaging system 1128 to capture images of different portionsof the article of dishware.

After imaging system 1128 captures the first set of images, roboticactuator 1122 places the article of dishware on a first conveyor belt1116 that is configured to move the article of dishware along a firstdirection 1138. In some embodiments, first conveyor belt 1116 is alsoreferred to as a “cleaning conveyor belt.”

FIG. 11 shows an article of dishware 1118 that has been placed on firstconveyor belt 1116 by robotic actuator 1122. First conveyor belt 1116transports article of dishware 1118 under one or more spray nozzles 1124that are configured to spray a fluid 1130 onto article of dishware 1118.In some embodiments, spray nozzles 1124 may be include one or moresteerable nozzles such as steerable nozzle 410. In some embodiments,fluid 1130 may be a dishwashing solution. In particular embodiments,spray nozzles 1124 may be configured to change a temperature, apressure, a chemical concentration, a spray duration, or othercharacteristics of fluid 1130, responsive to commands from processingsystem 1142. Spray nozzles 1124 essentially attempt to clean article ofdishware 1118. In some embodiments, spray nozzles 1124 may be steered totarget specific areas on article of dishware 1118, such as a soil orstain that may require additional cleaning as determined by processingsystem 1142 based on processing the first set of images. In other words,recirculation system 1100 includes targeted cleaning system featuresthat are discussed herein. Processing system 1142 may be configured touse any combination of the systems and methods discussed herein toachieve targeted cleaning of article of dishware 1118.

After cleaning, first conveyor belt 1116 continues to transport anarticle of dishware along first direction 1138. The article of dishwareis picked up by a robotic actuator 1132. For example, in FIG. 11,robotic actuator 1132 is shown to be picking up an article of dishware1134 that has been washed by spray nozzles 1124. In some embodiments,robotic actuator 1132 is configured to present article of dishware 1134in a field of view of second imaging system 1129 that is configured tocapture one or more images of article of dishware 1134. The one or moreimages captured by second imaging system 1129 may be referred to as asecond set of images. This second set of images is processed byprocessing system 1142 to determine a presence and location of any stainor soil on article of dishware 1134 that may not have been removed afterarticle of dishware 1134 has been washed by spray nozzles 1124. In someembodiments, processing system 1142 uses the CNN-based systems andmethods described herein to determine a presence and location of a stainor soil on article of dishware 1134. In some embodiments, roboticactuator 1132 may manipulate the article of dishware so as to change apose of the article of dishware in the field of view of second imagingsystem 1129. This allows second imaging system 1129 to capture images ofdifferent portions of the article of dishware. In some embodiments,imaging system 1128 and imaging system 1129 may be optically coupled toone or more optical filters as described herein.

In order to assist first imaging system 1128 and second imaging system1129 in capturing images of an article of dishware, recirculation system1100 may include one or more lights 1126 that are configured toilluminate an article of dishware presented in a field of view of firstimaging system 1128 or second imaging system 1129 by robotic actuator1122 or robotic actuator 1132 respectively. In some embodiments, lights1126 may include any combination of white light, UV lights, infraredlights, or any other light source as described herein.

After processing the second set of images, if processing system 1142determines that an article of dishware (e.g., article of dishware 1134)is clean, then processing system 1142 commands robotic actuator 1132 toplace the article of dishware in a dishrack 1106 containing a set ofclean articles of dishware 1108. On the other hand, if processing system1142 determines that the article of dishware is still stained, soiled ordirty, (i.e., if the article of dishware still contains a stain referredto as a “residual stain”), then processing system 1142 commands roboticactuator 1132 to place the article of dishware on a second conveyor belt1114 that moves the article of dishware in a second direction 1140,towards robotic actuator 1122. In some embodiments, second conveyor belt1114 is also referred to as a “recirculation conveyor.” FIG. 11 shows anarticle of dishware 1144 that has been placed by robotic actuator 1132onto second conveyor belt 1114 being returned to first robotic actuator1122.

As shown in FIG. 11, robotic actuator 1122 collects articles of dishwaresuch as an article of dishware 1120 off of second conveyor belt 1114 andresubmits, or recirculates the article of dishware for cleaning byrestarting the process of presenting the article of dishware in a fieldof view of first imaging system 1128, and so on. Robotic actuator 1122is thus configured to submit dirty articles of dishware for washingeither by picking up an article of dishware from stack of soileddishware 1102, or by picking up a still-dirty (after washing) article ofdishware off of second conveyor belt 1114. Recirculation system 1100 isa closed-loop system that, when coupled with targeted cleaningapproaches as discussed herein, embodies an automated, intelligentdishwashing system that is configured to perform intelligent cleaning onarticles of dishware by focusing on specific soiled regions.Intelligence associated with recirculation system 1100 also allowsrecirculation system 1100 to intelligently determine whether an articleof dishware is still soiled after washing and to submit any such soiledarticle of dishware for rewashing. FIG. 11 also shows drain water 1136from the dishwashing process from spray nozzles 1124 being collected ina water collection tank 1110 as waste water 1112.

In some embodiments, each of robotic actuator 1122 and robotic actuator1132 may be any combination of multi degree of freedom robotic arms,gantry robots, multi degree of freedom linear stage assemblies, and soon. In particular embodiments, each of robotic actuator 1122 and roboticactuator 1132 may be controlled by processing system 1142 (associatedcouplings are not shown in FIG. 11).

In some embodiments, a functionality of recirculation system 1100 may beaugmented by an algorithm to decide on very light cleaning or directpassing of articles of dishware based on the classification of the foodtypes, freshness of food soils, and the amount of food soils based onfood soil classification methods discussed herein. Such embodimentscould be used to optimize the throughput of recirculation system 1100,or the efficiency of energy consumption without compromising cleaningquality. For example, cups with fresh coffee or bowls may be quicklywashed by spraying with a small amount of water while cheese baked on aplate may need water focused on the soil for a longer period of time foreffective cleaning.

A more general method may combine the estimations before, during andafter washing to generate a summary of performance metrics to close theloop to adjust the machine parameters such as water, power, temperature,chemical dosage, and time to optimize desired key metrics such ascleaning quality, throughput, or energy consumption.

FIG. 12 is a block diagram depicting an embodiment of processing system1142 capable of operating a recirculation system that is configured todetermine whether an article of dishware that has been washed is stilldirty, and send the article of dishware back for additional cleaning ifthe article of dishware is determined to be dirty. In some embodiments,processing system 1142 includes a communication manager 1202 that isconfigured to manage communication protocols and associatedcommunication with external peripheral devices as well as communicationwithin other components in processing system 1142. For example,communication manager 1202 may be responsible for generating andmaintaining an interface between processing system 1142 and firstimaging system 1128. Communication manager 1202 may also managecommunication between the different components within processing system1142.

In some embodiments, processing system 1142 includes a memory 1204 thatis configured to store data associated with recirculation system 1100.Data stored in memory 1204 may be temporary data or permanent data. Insome embodiments, memory 1204 may be implemented using any combinationof hard drives, random access memory, read-only memory, flash memory,and so on. In particular embodiments, data stored in memory 1204 mayinclude positioning data associated with robotic actuator 1122, datafrom first imaging system 1128, and so on.

Processing system 1142 may also include an imaging system interface 1206that is configured to interface processing system 1142 with one or moreimaging systems such as first imaging system 1128 and second imagingsystem 1129. In some embodiments, imaging system interface 1206 mayinclude any combination of connectivity protocols such as IEEE 1394(FireWire), Universal Serial Bus (USB), and so on. Imaging systeminterface 1206 allows processing system 1142 to receive images from anassociated imaging system, while also sending commands to the imagingsystem (e.g., a command to capture an image when an article of dishwareis in a particular position in a field of view of the imaging system).

Processing system 1142 may also include a processor 1208 that may beconfigured to perform functions that may include generalized processingfunctions, arithmetic functions, and so on. Processor 1208 may also beconfigured to perform three-dimensional geometric calculations and solvenavigation equations in order to determine relative positions,trajectories, and other motion-related and position-related parametersassociated with manipulating an object by a robotic actuator.

A user interface 1210 may be included in processing system 1142. In someembodiments, user interface 1210 is configured to receive commands froma user or display information to the user. For example, commandsreceived from a user may be basic on/off commands, and may includevariable operational speeds. Information displayed to a user by userinterface 1210 may include, for example, system health information anddiagnostics. User interface 1210 may include interfaces to one or moreswitches or push buttons, and may also include interfaces totouch-sensitive display screens. In some embodiments, user interface1210 may allow a user to annotate an image of an article of dishware asdescribed herein. This annotation may be performed, for example, via atouchscreen, or a combination of a keyboard/mouse interface and a visualdisplay monitor.

In some embodiments, processing system 1142 includes a robotic actuatorcontroller 1212 that is configured to output actuation commands to arobotic actuator such as robotic actuator 1122. The commands output byrobotic actuator controller 1212 may include positioning commands,object gripping commands, object release commands, object repositioningcommands, and so on.

In some embodiments, processing system 1142 includes a lightingcontroller 1214 that is configured to alter any characteristics of lightused to illuminate an article of dishware that is being imaged by animaging system. Lighting controller 1214 may be configured to switchbetween different light sources, turn different light sources on or off,or configure physically-actuated optical filters in an incident orreflected light path as discussed herein. Details of lighting controller1214 are provided herein.

In some embodiments, processing system 1142 includes image analysissystem 214 that is configured to process images of an article ofdishware captured by, for example, imaging system 1128 to determine apresence of a stain, soil or dirt on an article of dishware. Imageanalysis system 214 may include subsystems that implement convolutionalneural network (CNN) based algorithms for processing the images, asdiscussed herein. A data bus 1216 interconnects all subsystemsassociated with processing system 1142, transferring data and commandswithin processing system 1142.

In some embodiments, processing system 1142 includes a spray nozzlecontroller 1216 that is configured to control a position of nozzles1124, as well as characteristics (e.g., temperature and pressure) offluid 1130 dispensed by nozzles 1124. Details of spray nozzle controller1216 are provided herein.

Some embodiments of processing system 1142 may include a dishrecirculation classifier 1218 that is configured to determine,responsive to processing system 1142 analyzing images of an article ofdishware post-wash, whether the article of dishware is dirty or clean.This determination is done using, for example, the CNN-based systems andmethod described herein. If an article of dishware is determined to beclean, the article of dishware is placed in a dishrack such as dishrack1106. If the article of dishware is determined to be dirty, the articleof dishware is sent (or “recirculated”) back to the start of thecleaning process.

FIG. 13 is a block diagram depicting an embodiment of a lightingcontroller 1214. In some embodiments, lighting controller 1214 isconfigured to switch between different light sources, turn differentlight sources on or off, or configure physically-actuated opticalfilters in an incident or reflected light path. In some embodiments,lighting controller 1214 includes a light source selector 1302 that isconfigured to select between a plurality of light sources such as UVlight sources, IR light sources, white light sources, and so on. In someembodiments, light source selector 1302 may turn on a single lightsource at a time. In other embodiments, light source selector 1302 mayturn on multiple, or all, light sources at a time. Light source selector1302 allows for illuminating an article of dishware with different kindsof light to capture images of the article of dishware in diverse lightconditions. This enables an associated stain detection system to detectand classify a stain or soil on the article of dishware with a higherprobability of success.

In some embodiments, lighting controller includes a light filterselector 1304 that is configured to insert, remove, or change one ormore optical filters (such as optical filter 108) in an incident lightpath such as incident light path 112. Light filter selector 1304 isessentially configured to switch between different filters (e.g., UVfilters, polarizing filters, and so on) placed between a light sourceand an article of dishware.

In some embodiments, lighting controller includes a camera filterselector 1306 that is configured to insert, remove, or change one ormore optical filters (such as optical filter 108) in a reflected lightpath such as reflected light path 114. Camera filter selector 1306 isessentially configured to switch between different filters (e.g., UVfilters, polarizing filters, and so on) placed between an article ofdishware and an imaging system.

In some embodiments, light filter selector 1304 and camera filterselector 1306 may use mechanical actuators or robotic actuators toperform their respective filter switching operations.

FIG. 14 is a block diagram depicting an embodiment of a spray nozzlecontroller 1216. In some embodiments, spray nozzle controller 1216 isconfigured to control operations of spray nozzles 1124 and varycharacteristics of fluid 1130. Specifically, spray nozzle controller1216 is configured to implement a targeted cleaning functionality. Someembodiments of spray nozzle controller 1216 include a fluid pressurecontroller 1402 that is configured to control a pressure associated withfluid 1130. The pressure of fluid 1130 varied responsive to acharacterization of an associated stain or soil on an article ofdishware, using the systems and methods described herein.

In some embodiments, spray nozzle controller 1216 includes a fluidtemperature controller 1404 that is configured to vary a temperature offluid 1130 in accordance with feedback from image analysis system 214.Fluid temperature controller 1404 further adds to targeted cleaning byadapting the temperature of fluid 1130 in accordance with acharacterization of a stain on an article of dishware.

Spray nozzle controller 1216 may also include a chemical dosagecontroller 1406 that is configured to vary a composition of fluid 1130.In some embodiments, fluid 1130 may be comprised of water, dishwashingliquid, rinsing agents, and other similar dishwashing agents. Chemicaldosage controller 1406 alters the composition of fluid 1130 responsiveto feedback from image analysis system 214 as a part of targetedcleaning. In some embodiments, image analysis system 214 may beconfigured to alter the composition of fluid 1130 (or fluid 422)responsive to image analysis system 214 determining a nature of thestain on the article of dishware. For example, cleaning a stainassociated with starch may entail treating the stain with a longerduration using hot water. On the other hand, a stain associated with oilor protein may require that a surfactant be included in fluid 1130 toeffectively dissolve and remove the stain.

In some embodiments, a duration for which fluid 1130 is sprayed on anarticle of dishware is controlled by a fluid jet duration controller1408 that is included in spray nozzle controller 1216. Controlling afluid jet duration in this way is an aspect of the targeted cleaningapproach discussed herein. In some embodiments, spray nozzle controller1216 includes a nozzle orientation controller 1410 that is configured tocontrol an orientation or spray direction of spray nozzles 1124. In someembodiments, spray nozzles 1124 include one or more steerable nozzlessuch as steerable nozzle 410. Nozzle orientation controller 1410 may beconfigured to independently steer any steerable nozzles associated withspray nozzles 1124 as a part of a targeted cleaning process.

FIG. 15A is a flow diagram depicting an embodiment of a method 1500 ofrecirculating articles of dishware. Method 1500 is associated withoperation of recirculation system 1100. In some embodiments, at 1502, aprocessing system (e.g., processing system 1142) commands a firstrobotic actuator (e.g., robotic actuator 1122) to grip an article ofdishware. The process of gripping an article of dishware is synonymouswith robotic actuator 1122 picking up an article of dishware. In someembodiments, the article of dishware is a soiled article of dishware. At1504, the processing system commands the first robotic actuator to placethe article of dishware in a field of view of a first imaging system(e.g., first imaging system 1128). Next, at 1506, the processing systemcommands the first imaging system to capture a first set of one or moreimages of different views of the article of dishware. This step issimilar to robotic actuator 1122 holding an article of dishware in afield of view of imaging system 1128. In some embodiments, the firstrobotic actuator may change an orientation of the article of dishware asviewed by the first imaging system over a series of images so that thefirst imaging system captures images of different portions of thearticle of dishware. At 1508, the processing system processes the firstset of one or more images to characterize soiling on the article ofdishware. This step is associated with operations of a stain detectionsystem such as stain detection system 100, and may use the CNN-basedsystems and methods described herein.

At 1510, the processing system commands the first robotic actuator toload the article of dishware onto a first conveyor belt (e.g., firstconveyor belt 1116). At 1512, the first conveyor belt moves the articleof dishware proximate to one or more dishwashing nozzles configured todispense a dishwashing solution. In some embodiments, the dishwashingnozzles are identical to spray nozzles 1124, and the dishwashingsolution is dispensed as fluid 1130. Next, at 1514 the processing systemcommands the dishwashing nozzles to adaptively dispense dishwashingsolution onto the article of dishware responsive to the characterizationof the soiling. This step corresponds to targeted cleaning, whereadaptive dispensing of dishwashing solution involves altering atemperature, a pressure, a chemical concentration or a spray duration ofthe dishwashing solution. In some embodiments, one or more of thedishwashing nozzles may be steerable nozzles similar to steerable nozzle410 that are configured to move or pivot on commands from the processingsystem, to target specific soiled regions on the article of dishware.Method 1500 then goes to A, with a continued description below.

FIG. 15B is a continued description of method 1500. Starting at A,method 1500 goes to 1516, where the first conveyor belt moves thearticle of dishware towards a second robotic actuator (e.g., roboticactuator 1132). Next, at 1518, the processing system commands the secondrobotic actuator to grip the article of dishware. At 1520, theprocessing system commands the second robotic actuator to place thearticle of dishware in a field of view of a second imaging system (e.g.,second imaging system 1129). At 1522, the processing system commands thesecond imaging system to capture a second set of one or more images ofdifferent views of the article of dishware. In some embodiments, thesecond robotic actuator may change an orientation of the article ofdishware as viewed by the second imaging system over a series of imagesso that the second imaging system captures images of different portionsof the article of dishware. At 1524, the processing system processes thesecond set of one or more images to characterize soiling on the articleof dishware. In some embodiments, this characterization may be performedby image analysis system 214 and the associated subsystems. At 1526,responsive to the characterization, method 1500 checks to see whetherthe article of dishware is soiled. Such a soiling is also referred to asa “residual stain.” If the article of dishware is not soiled, then themethod goes to 1530, where the processing system commands the secondrobotic actuator to place the article of dishware on a dishrack. On theother hand if, at 1526, the article of dishware is soiled, then themethod goes to 1528, where the processing system commands the secondrobotic actuator to place the article of dishware on a second conveyorbelt (e.g., second conveyor belt 1114) moving towards the first roboticactuator. The method 1500 then goes to B, and returns to 1502, where thesequence repeats till method 1500 determines that the article ofdishware is not soiled.

In some embodiments, after step 1514, rather than moving the article ofdishware towards the second robotic actuator, the article of dishware isleft proximate to the one or more dishwashing nozzles (the regionproximate to the one or more dishwashing nozzles is also referred to asa “cleaning section” or “cleaning area”). The second imaging system maythen capture the second set of one or more images of different views ofthe article of dishware. This second set of images is analyzed todetermine whether the article of dishware is soiled. If the article ofdishware is soiled then the article of dishware is left in the cleaningsection, and the cleaning process (i.e., step 1514) repeats, and so on,until analysis of the second set of images determines that the articleof dishware is clean. Once the article of dishware is determined to beclean, the first conveyor belt moves the article of dishware towards thesecond robotic actuator, which is commanded by the processing system toplace the article of dishware on the dishrack.

FIG. 16 is a flow diagram depicting an embodiment of a method 1600 toperform targeted cleaning on an article of dishware. At 1602, an imagingsystem captures at least one image of an article of dishware. At 1604, aprocessing system processes the at least one image to determine apresence and location of a stain on the article of dishware. In someembodiments the processing system is similar to processing system 106.At 1606, the method checks to determine if a stain is present on thearticle of dishware using the stain detection systems and methoddescribed herein. In some embodiments, this function may be performed byimage analysis system 214. If a stain is not present on the article ofdishware, method 1600 ends at 1608. If a stain is present on the articleof dishware, the method goes to 1610, where a cleaning system cleans thelocation of the stain on the article of dishware. In some embodiments, afunctionality of method 1600 is embodied by targeted cleaning system400.

While various embodiments of the present disclosure are describedherein, it should be understood that they are presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the disclosure.Thus, the breadth and scope of the present disclosure should not belimited by any of the described exemplary embodiments, but should bedefined only in accordance with the following claims and theirequivalents. The description herein is presented for the purposes ofillustration and description. It is not intended to be exhaustive or tolimit the disclosure to the precise form disclosed. Many modificationsand variations are possible in light of the disclosed teaching. Further,it should be noted that any or all of the alternate implementationsdiscussed herein may be used in any combination desired to formadditional hybrid implementations of the disclosure.

1-27. (canceled)
 28. A method for adjusting a machine parameter of adishwashing system, the method comprising: obtaining an image of anarticle of dishware; classifying food soils on the article of dishwareusing the image of the article of dishware and a neural network that istrained to classify food soils on articles of dishware based on imagesof articles of dishware; and adjusting a machine parameter of adishwashing system in response to the classification of the food soilson the article of dishware.
 29. The method of claim 28, wherein the foodsoils are classified in terms of freshness.
 30. The method of claim 28,wherein the food soils are classified in terms of an amount of foodsoils.
 31. The method of claim 28, wherein the food soils are classifiedin terms of a type of food soils.
 32. The method of claim 28, whereinthe machine parameter of a dishwashing system includes at least one ofwater, power, temperature, chemical dosage, and time.
 33. The method ofclaim 28, further comprising training the neural network by: labelingimages of articles of dishware with labels that corresponds to a timethat was taken to completely clean the respective articles of dishware;and training the neural network, using the labeled images, to classifyimages of articles of dishware with a food soil classification.
 34. Themethod of claim 33, further comprising tracking the time that was takenfor an article of dishware to be cleaned and using the time as a labelfor an image of the corresponding article of dishware, wherein the imagewas obtained before the article of dishware was cleaned.
 35. The methodof claim 33, further comprising labeling the images of articles ofdishware with labels that correspond to knowledge of food items that maybe placed in or on the article of dishware and training the neuralnetwork, using the labeled images, to classify images of articles ofdishware with a food soil classification.
 36. The method of claim 35,wherein the food soils on the article of dishware are classified usingthe image of the article of dishware and the knowledge of food itemsthat may be placed in or on the article of dishware.
 37. The method ofclaim 28, further comprising training the neural network by: labelingimages of articles of dishware with labels that corresponds to a timethat was taken to completely clean the respective articles of dishware;and training the neural network, using the labeled images, to classifyimages of articles of dishware with a degree of freshness.
 38. Themethod of claim 37, further comprising tracking the time that was takenfor an article of dishware to be cleaned and using the time as a labelfor an image of the corresponding article of dishware, wherein the imagewas obtained before the article of dishware was cleaned.
 39. The methodof claim 28, further comprising labeling the images of articles ofdishware with labels that correspond to knowledge of food items that maybe placed in or on the article of dishware.
 40. The method of claim 28,wherein the neural network is a convolutional neural network (CNN). 41.An automated dishwashing system comprising: a processing systemconfigured to: obtain an image of an article of dishware; classify foodsoils on the article of dishware using the image of the article ofdishware and a neural network that is trained to classify food soils onarticles of dishware based on images of articles of dishware; and adjusta machine parameter of a dishwashing system in response to theclassification of the food soils on the article of dishware.
 42. Amethod for operating a dishwashing system, the method comprising:obtaining an image of an article of dishware; estimating how long itwould take to clean an article of dishware using the image of thearticle of dishware and a neural network that is trained to estimatecleaning time based on images of articles of dishware; and cleaning thearticle of dishware according to the estimation.
 43. A method fortraining a neural network, the method comprising: labeling images ofarticles of dishware with labels that correspond to a time that wastaken to clean the respective articles of dishware; and training aneural network, using the labeled images, to classify images of articlesof dishware with a food soil classification.
 44. The method of claim 43,further comprising tracking the time that was taken for an article ofdishware to be cleaned and using the time as a label for an image of thecorresponding article of dishware, where the image was obtained beforethe article of dishware was cleaned.
 45. The method of claim 43, whereinthe food soils are classified in terms of freshness.
 46. The method ofclaim 43, wherein the food soils are classified in terms of an amount offood soils.
 47. The method of claim 43, wherein the food soils areclassified in terms of a type of food soils.
 48. The method of claim 43,further comprising labeling the images of articles of dishware withlabels that correspond to knowledge of food items that may be placed inor on the article of dishware.
 49. The method of claim 48, wherein theknowledge includes knowledge of a menu.
 50. The method of claim 48,wherein the knowledge includes knowledge of a business.
 51. A method fortraining a neural network, the method comprising: labeling images ofarticles of dishware with labels that correspond to a time that wastaken to clean the respective articles of dishware; and training aneural network, using the labeled images, to estimate a degree offreshness of articles of dishware.
 52. The method of claim 51, furthercomprising tracking the time that was taken for an article of dishwareto be cleaned and using the time as a label for an image of thecorresponding article of dishware, where the image was obtained beforethe article of dishware was cleaned.
 53. The method of claim 51, furthercomprising labeling the images of articles of dishware with labels thatcorrespond to a time that the corresponding article of dishware wasdropped off by a user of the article of dishware.